Finding an optimal dynamic treatment regime is a reinforcement learning problem [1]}, wherein an action by an agent, or a physician, changes both the immediate reward and the environment of the next stage and the learner, or the statistician, searches for the rule of actions that brings the most overall reward. Like many statistical learning methods, reinforcement learning problems are often solved using the Q-learning algorithm [2]}, [3]}, [4]}, [5]}, [6]}, which defines the value as the expected cumulative sum of discounted rewards and optimizes the set of rules using backward recursion.
